title: Detection of myocardial scar from the VCG using a supervised learning approach.
abstract: This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.
authors.forename: Christos
authors.initials: C
authors.lastname: Panagiotou
authors.forename: Sofia-Maria
authors.initials: SM
authors.lastname: Dima
authors.forename: Evangelos B
authors.initials: EB
authors.lastname: Mazomenos
authors.forename: James
authors.initials: J
authors.lastname: Rosengarten
authors.forename: Koushik
authors.initials: K
authors.lastname: Maharatna
authors.forename: John
authors.initials: J
authors.lastname: Gialelis
authors.forename: John
authors.initials: J
authors.lastname: Morgan
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac; D001185:Artificial Intelligence; D002921:Cicatrix / Q000175:diagnosis* / Q000503:physiopathology; D006321:Heart / Q000503:physiopathology*; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000503:physiopathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014672:Vectorcardiography / Q000295:instrumentation* / Q000379:methods